Surprisingly Easy Network Compression and Data Extension for Object Instance Detection

To detect instances in unstructured environment with mobile system, we develop a light weight but accurate learning model denoted as B-PA(BING Pruned Alexnet). Our method first utilizes BING(Binarized Normed Gradient) to compute bounding boxes, then builds a compressed network for recognition by pruning neurons and cutting fully connected layers on the original noted Alexnet. Addressing the problem that the training samples for instance detection are limited and of small variation, we extend the training data by combining data augmentation with synthetic generation. Our B-PA model takes only 5.3MB, which is 50 times smaller but with equivalent or even higher accuracy than the original Alexnet. Experiment results demonstrate that our method outperforms the state-of-art instance detection algorithms on WRGB-D Dataset and GMU Kitchen Dataset.